path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105186076/cell_17 | [
"image_output_1.png"
] | from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
X, y = make_regression(n_samples=4, n_features=1, n_informative=1, n_targets=1, noise=80, random_state=13)
from sklearn.linear_model import LinearRegression
reg = Linear... | code |
105186076/cell_14 | [
"image_output_1.png"
] | from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
X, y = make_regression(n_samples=4, n_features=1, n_informative=1, n_targets=1, noise=80, random_state=13)
from sklearn.linear_model import LinearRegression
reg = Linear... | code |
105186076/cell_10 | [
"image_output_1.png"
] | X.ravel()[:5] | code |
105186076/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
X, y = make_regression(n_samples=4, n_features=1, n_informative=1, n_targets=1, noise=80, random_state=13)
from sklearn.linear_model import LinearRegression
reg = Linear... | code |
105186076/cell_5 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X, y)
(reg.coef_, reg.intercept_) | code |
105207876/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
meta_vir = pd.read_csv('../input/rsna-2022-spine-fracture-detection-metadata/meta_train_with_vertebrae.csv')
y = meta_vir.iloc[:3408, 9:]
y.shape
del y['overall']
y.z_total.value_counts() | code |
105207876/cell_4 | [
"text_plain_output_1.png"
] | import glob as glob
len(glob.glob('../input/jpg-images/train_jpg/*jpg')) | code |
105207876/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
meta_vir = pd.read_csv('../input/rsna-2022-spine-fracture-detection-metadata/meta_train_with_vertebrae.csv')
meta_vir.head() | code |
105207876/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
meta_vir = pd.read_csv('../input/rsna-2022-spine-fracture-detection-metadata/meta_train_with_vertebrae.csv')
y = meta_vir.iloc[:3408, 9:]
y.shape
del y['overall']
y.z_total.value_counts()
labels = np.array(y)
labels.shape
... | code |
105207876/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
meta_vir = pd.read_csv('../input/rsna-2022-spine-fracture-detection-metadata/meta_train_with_vertebrae.csv')
y = meta_vir.iloc[:3408, 9:]
y.shape | code |
105207876/cell_8 | [
"image_output_1.png"
] | import pandas as pd
meta_vir = pd.read_csv('../input/rsna-2022-spine-fracture-detection-metadata/meta_train_with_vertebrae.csv')
y = meta_vir.iloc[:3408, 9:]
y.shape
y['overall'] = y['C1'] + y['C2'] + y['C3'] + y['C4'] + y['C5'] + y['C6'] + y['C7']
y['z_total'] = y['overall'].apply(lambda x: x if x == 0 else 1)
y['z... | code |
105207876/cell_17 | [
"text_plain_output_1.png"
] | model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=5) | code |
105207876/cell_14 | [
"text_html_output_1.png"
] | from torchvision import datasets,models,transforms
import PIL
import glob as glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
len(glob.glob('../input/jpg-images/train_jpg/*jpg'))
len(glob.glob('../input/jpg-images/test_jpg/*jpg'))
meta_vir = pd.read_csv('../input/rsna-2022-spine-fract... | code |
105207876/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
meta_vir = pd.read_csv('../input/rsna-2022-spine-fracture-detection-metadata/meta_train_with_vertebrae.csv')
y = meta_vir.iloc[:3408, 9:]
y.shape
del y['overall']
y.z_total.value_counts()
labels = np.array(y)
labels.shape | code |
105207876/cell_5 | [
"text_plain_output_1.png"
] | import glob as glob
len(glob.glob('../input/jpg-images/train_jpg/*jpg'))
len(glob.glob('../input/jpg-images/test_jpg/*jpg')) | code |
1001162/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
student_data = pd.read_csv('../input/student-mat.csv')
student_data.shape
student_data.dtypes
x = np.arange(0, 5, 1)
y = np.sin(x)
fig, ax = plt.subplots()
ind = np.arange(le... | code |
1001162/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
student_data = pd.read_csv('../input/student-mat.csv')
student_data.shape | code |
1001162/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
student_data = pd.read_csv('../input/student-mat.csv')
student_data.shape
student_data.dtypes
print(student_data[student_data.sex == 'F'].sex.count())
print(student_data[student_data.sex == 'M'].sex.count()) | code |
1001162/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1001162/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
student_data = pd.read_csv('../input/student-mat.csv')
student_data.shape
student_data.dtypes
plt.hist(student_data.studytime) | code |
1001162/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
student_data = pd.read_csv('../input/student-mat.csv')
student_data.shape
student_data.dtypes
x = np.arange(0, 5, 1)
y = np.sin(x)
plt.plot(student_data.studytime) | code |
1001162/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
student_data = pd.read_csv('../input/student-mat.csv')
student_data | code |
1001162/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
student_data = pd.read_csv('../input/student-mat.csv')
student_data.shape
student_data.dtypes | code |
16113958/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.v... | code |
16113958/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique() | code |
16113958/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :] | code |
16113958/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df.info() | code |
16113958/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.v... | code |
16113958/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.v... | code |
16113958/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum() | code |
16113958/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.v... | code |
16113958/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns | code |
16113958/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16113958/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :] | code |
16113958/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df.head() | code |
16113958/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.v... | code |
16113958/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.v... | code |
16113958/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.v... | code |
16113958/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.v... | code |
16113958/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.v... | code |
16113958/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df['Indicator Category'].value_counts(... | code |
16113958/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)] | code |
88082047/cell_21 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"im... | from pycaret.classification import * | code |
88082047/cell_13 | [
"text_html_output_1.png"
] | !pip install autoviz | code |
88082047/cell_9 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_house = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_HOUSEHOLD_VICTIMIZATION_1993-2014.csv')
df_personal = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_PERSONAL_VICTIMIZATION_1993-2014.csv')
df_dict = pd.read_csv(... | code |
88082047/cell_4 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
88082047/cell_30 | [
"image_output_1.png"
] | nb = create_model('nb')
plot_model(nb, plot='pr') | code |
88082047/cell_20 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | pip install pycaret --ignore-installed llvmlite numba | code |
88082047/cell_29 | [
"text_html_output_1.png"
] | nb = create_model('nb')
plot_model(nb, plot='auc') | code |
88082047/cell_19 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | from autoviz.AutoViz_Class import AutoViz_Class
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_house = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_HOUSEHOLD_VICTIMIZATION_1993-2014.csv')
df_personal = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_PERSONAL_VIC... | code |
88082047/cell_28 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | nb = create_model('nb')
plot_model(nb, plot='confusion_matrix') | code |
88082047/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_house = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_HOUSEHOLD_VICTIMIZATION_1993-2014.csv')
df_personal = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_PERSONAL_VICTIMIZATION_1993-2014.csv')
df_dict = pd.read_csv(... | code |
88082047/cell_31 | [
"image_output_1.png"
] | nb = create_model('nb')
optimize_threshold(nb) | code |
88082047/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_house = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_HOUSEHOLD_VICTIMIZATION_1993-2014.csv')
df_personal = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_PERSONAL_VICTIMIZATION_1993-2014.csv')
df_dict = pd.read_csv(... | code |
88082047/cell_14 | [
"text_plain_output_1.png"
] | from autoviz.AutoViz_Class import AutoViz_Class | code |
88082047/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_house = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_HOUSEHOLD_VICTIMIZATION_1993-2014.csv')
df_personal = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_PERSONAL_VICTIMIZATION_1993-2014.csv')
df_dict = pd.read_csv(... | code |
88082047/cell_27 | [
"text_html_output_1.png"
] | nb = create_model('nb') | code |
73091762/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.drop(columns=['null1'], inplace=True)
df['day_added'] = ... | code |
73091762/cell_20 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
import re
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(column... | code |
73091762/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.head(10) | code |
73091762/cell_2 | [
"text_html_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
73091762/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.drop(columns=['null1'], inplace=True)
df['day_added'] = ... | code |
73091762/cell_19 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.drop(columns=['null1'], inplace=True)
df['day... | code |
73091762/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape | code |
73091762/cell_18 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id... | code |
73091762/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.head() | code |
73091762/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.drop(columns=['null1'... | code |
73091762/cell_17 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id... | code |
73091762/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.drop(columns=['null1'... | code |
73091762/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.drop(columns=['null1'], inplace=True)
df['day_added'] = ... | code |
129024743/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.neural_network import MLPRegressor
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
import seaborn as sns
from sklearn.preprocessing import ... | code |
129024743/cell_33 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn.datasets import fetch_openml
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_... | code |
129024743/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.datasets import fetch_openml
import matplotlib.pyplot as plt
digits = datasets.load_digits()
plt.figure(1, figsize=(3, 3))
plt.imshow(digits.images[-1], cmap=plt.cm.gray_r, interpolation='nearest')
plt.show() | code |
129024743/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = MLPClassifier(random_state=1)
clf.fit(X2_train, y2_train)
clf.score(X2_test, y2_test)
scaler = StandardScaler()
scaler.fit(X... | code |
129024743/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.neural_network import MLPRegressor
from sklearn.neural_network import MLPRegressor
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_t... | code |
129024743/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.neural_network import MLPClassifier
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test) | code |
129024743/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = MLPClassifier(random_state=1)
clf.fit(X2_train, y2_train)
clf.score(X2_test, y2_test)
scaler = StandardScaler()
scaler.fit(X... | code |
129024743/cell_38 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn.datasets import fetch_openml
from sklearn.datasets import fetch_openml
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
... | code |
129024743/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.datasets import fetch_openml
X2, y2 = fetch_openml('mnist_784', version=1, return_X_y=True, as_frame=False) | code |
129024743/cell_31 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.neural_netw... | code |
129024743/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.neural_network import MLPClassifier
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = MLPClassifier(random_state=1)
clf.fit(X2_train, y2_train)
clf.score(X2_test, y2_test) | code |
129024743/cell_36 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import datasets
from sklearn.datasets import fetch_openml
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_... | code |
105206572/cell_13 | [
"text_html_output_1.png"
] | import datetime as dt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv(... | code |
105206572/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/customer-tran... | code |
105206572/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/customer-tran... | code |
105206572/cell_11 | [
"text_html_output_1.png"
] | import datetime as dt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv(... | code |
105206572/cell_19 | [
"text_html_output_1.png"
] | import datetime as dt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv(... | code |
105206572/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
105206572/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/customer-tran... | code |
105206572/cell_15 | [
"text_html_output_1.png"
] | import datetime as dt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv(... | code |
105206572/cell_17 | [
"text_html_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as... | code |
105206572/cell_10 | [
"text_plain_output_1.png"
] | import datetime as dt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv(... | code |
105206572/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/customer-tran... | code |
90103033/cell_21 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train) | code |
90103033/cell_23 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
model.predict(X_test)
model.score(X_test, y_test) | code |
90103033/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = df[df.left == 1]
left.shape
retained = df[df.left == 0]
retained.shape | code |
90103033/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = df[df.left == 1]
left.shape
retained = df[df.left == 0]
retained.shape
df.groupby('left').mean() | code |
90103033/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = df[df.left == 1]
left.shape
retained = df[df.left == 0]
retained.shape
df.groupby('left').mean()
df_new = df[['satisfaction_level', 'average_montly_hours', 'promotion_last_5years', 'salary']]
dummy_salary = pd.get_dummies(df_new... | code |
90103033/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = df[df.left == 1]
left.shape
retained = df[df.left == 0]
retained.shape
df.groupby('left').mean()
df_new = df[['satisfaction_level', 'average_montly_hours', 'promotion_last_5years', 'salary']]
dummy_salary = pd.get_dummies(df_new... | code |
90103033/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
df.head() | code |
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